Dose–response association between dietary patterns and gestational diabetes mellitus risk: A systematic review and meta‐analysis of observational studies

Abstract Although dietary factors are relevant modifiable risk factors for gestational diabetes mellitus (GDM), the exact association between dietary patterns and GDM remains controversial. Therefore, a systematic review and dose–response meta‐analysis of observational studies were conducted to summarize the association between dietary patterns and risk of GDM. PubMed, Scopus, Web of Science, Cochrane Library, and EMbase databases were systematically searched for publications up to March 10, 2020. All observational studies which assessed the risk of GDM according to the categories of healthy or unhealthy dietary patterns derived by either a priori or a posteriori methods were eligible to be included. Pooled effect sizes for the highest vs. lowest categories of healthy/unhealthy dietary patterns were calculated using the random‐effects model. Linear and nonlinear dose–response analyses were performed to determine dose–response associations. Thirty‐one studies were included, of which 26 studies (80,849 participants) assessed healthy dietary pattern and 15 studies (32,965 participants) assessed the unhealthy dietary pattern. Individuals with a higher adherence to the healthy dietary pattern were less likely to be affected by GDM (RR = 0.86; 95% CI: 0.76–0.96; I 2 = 56.2%). There was a marginally significant association between unhealthy dietary patterns and GDM risk (RR = 1.28; 95% CI: 0.99–1.67; I 2 = 74.7). Significant linear associations were observed between healthy (p = .011) and unhealthy (p = .009) dietary patterns and GDM risk. Pregnant women with a healthier dietary pattern (a diet rich in fruits, vegetables, and whole grains) had lower risk for GDM. In contrast, higher adherence to an unhealthy dietary pattern was associated with increased risk of GDM. Further longitudinal studies are needed to confirm the results.


| INTRODUC TI ON
Gestational diabetes mellitus (GDM) is defined as diabetes diagnosed in the second or third trimester of pregnancy that was not clearly overt diabetes prior to gestation (American Diabetes Association [ADA], 2020). GDM is one of the most common pregnancy complications worldwide with a global prevalence of 14.0% (varying from 7.5% to 27.0% among different areas; Wang et al., 2022). In 2021, the International Diabetes Federation (IDF) estimated that 16.7% of live births were affected by hyperglycemia in pregnancy, among which about 75%-90% was due to GDM (Atlas IDFD, 2021).
GDM has notable adverse effects on the health status of mothers and their offspring during pregnancy, delivery, or even later in life. Risk factors for GDM include several factors, such as maternal age, excess body weight, race, family history of diabetes, and lifestyle (Mirghani Dirar & Doupis, 2017) and the combination of various risk factors can synergistically increase the risk of GDM (Popova et al., 2015). Previous evidence suggested that dietary patterns were associated with the risk of developing GDM (Bao, 2016;Mirghani Dirar & Doupis, 2017;Reader et al., 2006) but findings were not fully consistent (Izadi et al., 2016;Tobias et al., 2012;Tryggvadottir et al., 2016). For example, while some studies have shown that higher adherence to the Western dietary pattern (high in red and processed meat, refined grains, sugar, and fried foods) was associated with an increased risk of GDM (de Seymour et al., 2016;Hassanizadeh et al., 2020;He et al., 2015;Izadi et al., 2016;Schoenaker et al., 2015;Zareei et al., 2018;Zhang & Ning, 2011), some others suggested a null association (de Seymour et al., 2016;He et al., 2015). A similar discrepancy has also been reported for healthy dietary patterns which are highly loaded with fruits, vegetables, low-fat dairy products, and whole grains de Seymour et al., 2016;Flynn et al., 2016).
To date, two meta-analyses and systematic reviews have been tried to summarize the association between dietary patterns and GDM (Hassanizadeh et al., 2020;Kibret et al., 2019). They showed that greater adherence to Western dietary patterns was significantly and positively associated with the risk of GDM, while healthy dietary patterns were inversely related to the risk of GDM (Hassanizadeh et al., 2020;Kibret et al., 2019). However, Kibret et al.'s study is based on a few effect sizes for dietary patterns (Hassanizadeh et al., 2020), and Hassanizadeh et al.'s study has separately analyzed healthy/unhealthy dietary patterns without combining these patterns (Kibret et al., 2019). In addition, these meta-analyses missed some eligible publications and focused on cohort studies. Moreover, none of them examined the dose-response association (Kibret et al., 2019;Hassanizadeh et al., 2020).
To the best of our knowledge, there is no information regarding the strength and shape of a dose-response relationship between dietary patterns and risk of GDM. Therefore, a systematic review and dose-response meta-analysis were conducted to summarize the association between dietary patterns (healthy, unhealthy) and risk of GDM in all available observational studies.

| ME THODS
The protocol of this review and meta-analysis follows the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement (Moher et al., 2009). Table 1 shows the inclusion and exclusion criteria based on the PICOS framework. The search was carried out using PubMed, Scopus, Web of Science, Cochrane Library, and EMbase databases.

| Search strategy
It was updated until March 10, 2020, using the following keywords: ("diet* pattern*" OR "eating pattern*" OR "food* pattern*" OR "diet* habit*" OR diet* OR "maternal diet* pattern*")) AND ("Diabetes, Gestational" OR "Gestational Diabetes" OR "Diabetes Mellitus, Gestational" OR "Pregnancy-Induced Diabetes"). In addition, the references cited in the relevant articles or published reviews were carefully evaluated by a manual search for additional pertinent studies. Where there was a lack of information in the publications, the corresponding authors were contacted to complete data. No date, language, or country restrictions were applied. The Mendeley reference manager (version 1.19.4) was used in the review process.

| Study selection
Two independent reviewers (R.R. and Z.H.) performed study screening and exclusion procedures. Studies were included based on the following selection criteria: (1) evaluation of either a priori or a posteriori dietary patterns as the exposure of interest; (2) reporting the risk of GDM as the outcome of interest; (3) being an original observational study (e.g., cohort study, case-control study, or crosssectional study); (4) reporting the usable risk estimates (e.g., relative risks [RRs], hazard ratios [HRs], or odds ratios [ORs]) or presenting necessary data for estimation; and (5) focusing on the general population of pregnant women. Studies were excluded if they (1) investigated nutrients, foods, or food groups separately or did not meet the definition of healthy and unhealthy dietary patterns in the present study; (2) did not report the risk of GDM as an outcome; (3) were not original research (i.e., letters to the editor, editorial or review articles, or unpublished data); and (4) did not report necessary risk estimates (RRs, HRs, or ORs). When two reviewers disagreed, the selection of studies or data extraction was resolved through discussion and consensus.

| Data extraction and quality assessment
In the current systematic review and meta-analysis, the following information was extracted from the included publications: the author's first name, country, year, study design, mean age or age range of participants, sample size, follow-up duration for longitudinal studies, instruments used to assess dietary intakes, race/ethnicity, time period to assess dietary intakes, the method used to GDM diagnosis, the method used to define dietary patterns, the identified dietary patterns, main findings (effect and 95% CI), and adjusted confounders. For some studies, the corresponding author was contacted to collect incomplete data. If several dietary patterns were reported in a study, the patterns were included that matched well with the definition in the study. Accordingly, the healthy dietary pattern was defined as a diet with high consumption or factor loading of fruits, vegetables, whole grains, and fish, while the unhealthy dietary pattern was defined as a diet with high consumption or factor loading of red or processed meat, refined grains, sweets, and fast foods. The number and name of dietary patterns varied in different studies, so two of the patterns were selected that were most similar to the definitions in the study and they were labeled as healthy or unhealthy.
The risk of bias for included studies in the meta-analysis was assessed by the National Institutes of Health (NIH) Quality Assessment Tool (National Institutes of Health). A 14-item form for cohort studies and a 12-item version for case-control studies were utilized (National Institutes of Health). A modified version of the NIH form was used to assess the risk of bias in the cross-sectional studies. This modified tool assesses the quality of cross-sectional studies based on 11 of 14 items for cohort studies. Two reviewers (R.R. & SH.S.) evaluated the risk of bias independently, and inconsistencies were resolved by a third researcher (Z.H.). The questions on these forms generally included items such as the research question or objective, definition of the study population, participation rate, recruitment criteria, inclusion and exclusion criteria, sample size, exposure assessment, timeframe, exposure levels, outcome assessment, loss to follow-up, and confounding variables. According to these forms, the maximum scores for cohort, case-control, and cross-sectional studies are equal to 14, 12, and 11, respectively.
Each study was rated as good (75% or more of the total score, i.e., a low risk of bias), fair (more than 50% but less than 75% of the total score i.e., moderate risk of bias), or poor (50% or less of the total score, i.e., high risk of bias).

| Statistical analysis
Since the incidence and prevalence of GDM are relatively low (<10%) (Behboudi-Gandevani et al., 2019;Marí-Sanchis et al., 2018), all effect sizes in the current meta-analysis were regarded equivalent and pooled as one effect size (i.e., RR). The RR and 95% CIs of the highest level of a healthy/unhealthy dietary pattern compared to the lowest level of a healthy/unhealthy dietary pattern were extracted. To evaluate the association between healthy/unhealthy dietary patterns and the risk of GDM, the pooled RR and 95% CIs were estimated by the random-effects model. To evaluate heterogeneity between studies, the Cochran Q test and I 2 statistic were employed (Higgins et al., 2003). An I 2 value equal to or above 75% was regarded as an indication of substantial heterogeneity (Higgins et al., 2003).
Subgroup analysis was then performed based on the study design (cohort, cross-sectional, and case-control), geographical region of the study (Western countries/non-Western countries), dietary tool (FFQ, dietary recall, and dietary record), energy adjustment (yes/no), the method used to define dietary pattern (a posteriori and a priori), and the time period of dietary intake evaluation (pre-pregnancy or during the pregnancy). A leave-one-out sensitivity analysis was carried out to explore the extent to which a particular study might have affected the result. Potential publication bias was assessed by Begg and Egger's tests (Egger et al., 2008;Sutton et al., 2000). A doseresponse meta-analysis was used to estimate the trend of GDM risk across categories of healthy/unhealthy dietary patterns using the proposed method by Xu and Doi (Xu & Doi, 2018). The parameters of this model are estimated using the inverse variance weighted least squares regression with cluster robust error variances (REMR model), and this model does not require any information in terms of the correlation structure of regression coefficients (Xu & Doi, 2018).
A dose-response model with a quadratic trend and also restricted cubic splines with three knots were used to examine a possible nonlinear association between dietary patterns and GDM risk. In the modeling process, the departure from a linear model was also tested.

Adjustment for confounders Score
During pregnancy Plasma glucose level > = 5.1 mmol/L Reduced rank regression "high refined grains, fats, oils and fruit juice" pattern: high intakes of refined grains, solid fats, oils, and fruit juice. "high nuts, seeds, fat and soybean; low milk and cheese" pattern: high intakes of nuts and seeds, solid fats, soybean products and low intakes of milk and cheese. "high added sugar and organ meats; low fruits, vegetables and seafood" pattern: high intakes of added sugars and organ meats and low intakes of fruits and vegetables and seafood

Adjustment for confounders Score
During pregnancy 75 g 2-h oral glucose tolerance test Factor analysis "Traditional pattern (TFD)": high intakes of tubers, vegetables, fruits, rice, red meat, eggs, and nuts. "Sweet foods pattern (TFD)": high intakes of pastry and candy, sweet beverages, shrimps, crabs, mussels, fruits, and red meat. "Fried food-beans pattern (TFD)": high intakes of fried foods, beans and products, and dairy products, and a low intake of organ meats. "Whole grain-seafood pattern (TFD)": high intakes of whole grains, shrimps, crabs, mussels, nuts, and seaweed, and a low intake of eggs, dairy products, and rice

Adjustment for confounders Score
During pregnancy 75 g 2-h oral glucose tolerance test Factor analysis "Vegetable-based pattern": high intakes of root vegetables, gourd/ melon family vegetables, freshwater fish, leafy and cruciferous vegetables, and red meat. "Poultry-and-fruit-based pattern": high intakes of poultry, fresh fruit, processed fruit, soups and meat innards. "Sweet-based pattern": high intakes of biscuits, pastries, cakes, bread and deep-sea fish and seafood products. "Plant-protein-based pattern": high intakes of soya milk, legumes, beans or bean products, buns and rice information, no response was received. Finally, Flynn et al.'s (2016) study is a randomized controlled trial where the association between baseline dietary patterns and the risk of GDM was regarded as a cross-sectional study in our analysis.

| Healthy dietary patterns and risk of GDM
The results of the meta-analysis of the healthy dietary pattern are shown in Figure 2.

| Risk of bias
According to the quality assessment tools, all included studies in the meta-analysis had a low risk of bias (Table 2). Accordingly, the maximum scores for cohort, cross-sectional, and case-control studies were considered equal to 14, 11, and 12, respectively. There is a large body of evidence indicating an inverse association between healthful dietary patterns and diabetes risk and a direct link between unhealthy eating patterns and diabetes risk These findings are also compatible with two earlier meta-analyses (Hassanizadeh et al., 2020;Kibret et al., 2019). However, one of these studies is based on only 5 and 4 effect sizes for healthy and unhealthy eating patterns (Kibret et al., 2019), respectively, and another one has separately analyzed different dietary patterns without combining these patterns (Hassanizadeh et al., 2020). Moreover, they have Several reasons that may explain these associations through the molecular mechanisms have not been well established. Unhealthy diets are mainly high in red and processed meats. A higher intake of meat, in particular processed red meat, is associated with a higher intake of nitrites which might be converted into nitrosamines (Lijinsky, 1999) in stomach or food products. Nitrosamines are toxic for beta-cells (Lijinsky, 1999) and potentially can induce type 2 diabetes (Ito et al., 1999). Besides, advanced glycation end products (AGEs), formed through heating and processing in meat and high-fat products , are implicated in the development of diabetes mellitus possibly via their stimulating effect on inflammatory pathways . Red and processed meats are also rich sources of saturated fatty acids and heme iron that may increase oxidative stress and beta-cell damage (Zhang et al., 2006).
A large prospective cohort study demonstrated that adjustment for red and processed meat disappeared the significant adverse association between the Western dietary pattern and GDM risk, while red and processed meat were independently linked to the risk of GDM (Zhang et al., 2006). Another possible explanation might be related to the close and positive correlation between Western dietary patterns and fasting glucose, insulin, and C peptide (Fung et al., 2001).
Therefore, prepregnancy beta-cell dysfunction and insulin resistance in women who develop GDM may compromise their capacity to adapt to the metabolic changes (i.e., increased insulin resistance) which predominantly occur in late pregnancy (Buchanan, 2001;Metzger et al., 2007). Additionally, various constituents of fruits, vegetables, and whole grains in a healthy diet (i.e., antioxidants, phytochemicals, and fiber) may decrease GDM risk through their reductive effects on oxidative stress and insulin demand (Hamer & Chida, 2007). The lower glycemic load of healthy diets in contrast with unhealthy dietary patterns may also provide another reason in support of our findings since the glycemic load is a strong predictor of postprandial glycemic response (Pustozerov et al., 2020).
According to the subgroup analysis, the results for healthy dietary patterns depend on study design, methods used to assess dietary patterns, and the time period of data collection (prepregnancy or during pregnancy). The nonsignificant association in cross-sectional and case-control studies and in studies that collected dietary data during pregnancy might be related to behavioral changes after the diagnosis of GDM (Okely et al., 2019) that would result in favorable dietary changes. The null association between GDM and dietary patterns determined by a posteriori methods may be attributed to the variations in factor loading of food items which varied between studies. In addition, they may be predominantly different in food composition despite having some shared components.
Results of subgroup analysis based on adjustment for energy influenced both results for healthy and unhealthy eating patterns.
While the associations were statistically significant in studies that assessed energy intake, no significant association was found in studies that did not. Indeed, variation in total energy intake due to body size, physical activity, and metabolic rate inevitably affects nutrient intake, and thereby obscure or even reverse the associations (Willett et al., 1997). The differences in geographical regions might be attributed to differences in living conditions, socioeconomic status, and other lifestyle factors that vary by country.
It is acknowledged that the analysis has some limitations which should be taken into consideration when interpreting the results.
First, dietary intakes were examined through self-reported tools which are prone to recall bias, although they are generally accepted.
Furthermore, variations in the instruments used to assess dietary intakes can lead to a combination of measurement errors and misclassification of participants which consequently affect the results.
Besides this, variations in GDM definitions between medical organizations can affect the generalizability of our findings and should be accounted for in medical implementation (Popova et al., 2016). Third, since there is no cutoff level to determine adherence to a healthy or unhealthy diet, it is not possible to conclude how much adherence would be enough to effectively reduce GDM risk. Forth, although the most adjusted estimates were used in the analysis, because of the observational design of included studies, the effects of residual and unknown confounders cannot be completely ruled out.
The meta-analysis also has several strengths. A comprehensive literature search was conducted, and in comparison with the previously published meta-analysis, more relevant articles were identified and included in the analysis, and also a dose-response analysis was performed. The maximally adjusted models were enrolled in the analysis to consider the effect of various confounders, and various potential sources of heterogeneity were examined in the study.
Moreover, the results of sensitivity analysis suggested that the results were robust, particularly for healthy dietary pattern. The lack of publication bias is another strength of this study.

| CON CLUS ION
The findings of this meta-analysis support a strong inverse association between the healthy dietary pattern and GDM risk, whereas an unhealthy dietary pattern was associated with a tendency toward higher risk for GDM. However, the results are based on observational studies and limited numbers of prospective cohort studies which may not exactly allow inferring the causality, and there is a need for further prospective cohort studies and clinical trials to explore the causality between healthy and unhealthy dietary patterns and GDM.

ACK N OWLED G M ENT
The authors are responsible for the content and writing of the paper.

FU N D I N G I N FO R M ATI O N
This study was supported by Isfahan University of Medical Sciences (IR.MUI.RESEARCH. REC.1398.800;Project Number: 198247).

CO N FLI C T O F I NTE R E S T
The authors declare that there are no conflicts of interest.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are available from the corresponding author upon reasonable request.

E TH I C S S TATEM ENT
This study does not involve any human or animal testing.